Early identification of ischemic stroke in noncontrast computed tomography. (July 2019)

• Record Type: Journal Article
• Authors: Wu, Guoqing; Lin, Jixian; Chen, Xi; Li, Zeju; Wang, Yuanyuan; Zhao, Jing; Yu, Jinhua
• Is Part Of: Biomedical signal processing and control. Volume 52(2019)
• Page Start: 41
• Page End: 52
• Publication Date: 2019-07
• Subjects: Ischemic stroke -- Radiomics -- Sparse representation -- Noncontrast computed tomography; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.03.008
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An effective radiomic system is proposed to identify the early ischemic stroke that is invisible to the radiologist in ncCT images. A novel multi-scale symmetry image patch classification model is designed to detect the stroke regions. A MAP model integrating the multi-scale and spatial constraint information is built to improve the identification performance. Abstract: Early identification of stroke is critical for the treatment and subsequent recovery. Non-contrast computed tomography (ncCT) is a routinely employed imaging modality for stroke evaluation. However, the early identification of stroke in ncCT images is very difficult, since there are subtle differences between lesion and healthy tissue during the hyperacute phase. In this paper, an image patch classification-based method was developed to detect the early ischemic stroke that is invisible to the radiologist in ncCT. First, we proposed radiomics-based patch classification model to identify whether each patch in ncCT is stroke region or not. To improve the identification accuracy, a symmetry image patch classification was developed, in which an image patch in one brain hemisphere was combined with its contralateral image patch for classification. Second, based on the spatial dependencies between neighboring patches, we built a maximum a posteriori identification model which integrated the spatial constraint information to improve the identification performance. Finally, after performing morphological post-processing, we defined the detected results containing more than 300 voxels as the stroke region. 108 stroke cases that were all invisible to radiologists in ncCT were enrolled in the study and divided into one training cohort and two independent testing cohorts to validate the proposed method. The proposed method has achieved identification accuracies of 76.67% and 75.00% on the two independent testing cohorts, respectively. The results proved the potential of the proposed radiomics model in the task which possessed great clinical values.